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Business, Finance & Law      Management

Stop the Data Madness!: Lessons from a Lifetime in Data Management

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Book Details
Language
English
Publishers
Technics Publications (29 July 2024)
Weight
0.32 KG
Publication Date
29/07/2024
ISBN-10
1634624998
Pages
237 pages
ISBN-13
9781634624992
Dimensions
15.24 x 1.37 x 22.86 cm
SKU
9781634624992
Author Name
Merrill Albert (Author)
Merrill has had the ultimate career of working in data. Starting with a BMath degree from the University of Waterloo, Merrill has worked consistently with companies across all industries to help them better manage their data to drive value from it. Being exposed to a wide variety of companies, Merrill has been able to see the same problems happening time and again, often caused by people not recognizing the value data brings to an organization and what happens when the data is mismanaged. Through education, coaching, and hands-on work, Merrill has been able to share her knowledge with companies to help them better improve their data infrastructure and prevent creating the next data crime.Read more about this authorRead less about this author
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Learn what’s going wrong with data and how to fix it. My last book was about data crimes.

We have to solve those crimes and prevent them from happening again. Data is too valuable to leave unattended.

Pick and choose stories to read by subject, including data management, data lifestyle, data governance, data architecture, master data management, data modeling, data lakes, metadata, data quality, data privacy, data retention, and analytics. Use the stories to rethink what you’ve been doing and if you need or want to take a new approach.

These stories come from a lifetime of working with data. I’ve seen the good and the bad.

Keep the good, improve the bad, and take a more efficient and effective approach to dealing with data. Life revolves around data.

Unfortunately, most people don’t realize this and take data for granted. Without devoting the necessary attention to data, you risk everything based on your data.

How do you expect to make good business decisions if you’re basing them on bad data? How do you expect to get good reports, analytics, and AI without good data?. .

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